Enhanced crop yield prediction using Monte Carlo method and binary cuckoo search

Chellammal Surianarayanan; Kodimalar Palanivel; K. Mani

doi:10.26637/MJM0804/0074

Abstract

The yield of crops is influenced by various factors such as weather conditions, soil characteristics, irrigation facility, solar radiation, fertilizer application, tillage, etc. Accurate prediction of crop yield is an important issue in agriculture as un-presented changes in yield will significantly influence food supply and market prices. Data pre-processing and selection of relevant features is an essential step while perform prediction using machine learning algorithms. In this work, Monte Carlo simulation for random selection of data and binary cuckoo search for relevant feature selection are used with an objective of enhancing the accuracy of prediction using multiple linear regression technique. Experimental results are discussed.

Keywords:

Binary cuckoo search, Monte Carlo method,, multiple linear regression, prediction of crop yield.

Mathematics Subject Classification:

Mathematics

Authors Imprint References Funding Related Articles Downloads

Chellammal Surianarayanan Department of Computer Science Bharathidasan University Constituent Arts & Science College, Tiruchirappalli-621303, Tamil Nadu, India. Affiliated to Bharathidasan University Tiruchirappalli, Tamil Nadu, India.
Kodimalar Palanivel Department of Computer Science Bharathidasan University Constituent Arts & Science College, Tiruchirappalli-621303, Tamil Nadu, India. Affiliated to Bharathidasan University Tiruchirappalli, Tamil Nadu, India.
K. Mani Department of Computer Science, Nehru Memorial College, Puthanampatti-621007, Tiruchirappalli, Tamil Nadu, India.

Pages: 1771-1776
Date Published: 01-01-2020
Vol. 8 No. 04 (2020): Malaya Journal of Matematik (MJM)

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